Category Archives: SAT

CryptoMiniSat 5.6.3 Released

The latest CryptoMiniSat, version 5.6.3 has been released. This release marks the 12’000th commit to this solver that has weathered more than I originally intended it to weather. It’s been an interesting ride, and I have a lot to thank Kuldeep and NSCC‘s ASPIRE-1 cluster for this release. I have burned over 200k CPU hours to make this release, so it’s a pretty well-performing release (out-performing anything out there, by a wide margin), though I’m working very hard to make sure that neither I nor anyone else will have to burn anything close to that to make a well-performing SAT solver.

The solver has some interesting new algorithms inside, the most interesting of which is Gauss-Jordan elimination using a Simplex-like method, generously contributed by Jie-Hong Roland Jiang and Cheng-Shen Han from the National Taiwan University. This addition should finally settle the issues regarding Gaussian vs Gauss-Jordan elimination in SAT solvers. Note that to use this novel system, you must configure with “cmake -DUSE_GAUSS=ON ..” and then re-compile.

What’s also interesting is what’s not inside, though. I have been reading (maybe too much) Nassim Taleb and he is very much into via negativa. So I tried removing algorithms that have been in the solver for a while and mostly nobody would question if they are useful. In the end I removed the following algorithms from running by default, each removal leading to better solving time:

  • Regular probing. Intree probing is significantly better, so regular probing is not needed. Thanks Matti/Marijn/Armin!
  • Stamping. This was a big surprise, especially because I also had to remove caching, which is my own, crappy (“different”) version of stamping. I knew that it wasn’t always so good to have, but damn. It was a hard call, but if it’s just slowing it down, what can I do. It’s weird.
  • Burst searching. This is when I search for a short period with high randomness all over the search space. I thought it would allow me to explore the search space in places where VSIDS/Maple doesn’t. Why this is slowing the solver down so much may say more about search heuristics/variable bumping/clause bumping than anything.
  • Note that I never had blocked clause elimination. It doesn’t work well for incremental solving. In fact, it doesn’t work at all, though apparently the authors have some new work that allows it to work, super-interesting!

I’m nowadays committed to understanding this damned thing rather than adding another impossible-to-explain magic constant  to make the solving 10% faster. I think there is interesting stuff out there that could be done to make SAT solvers 10x, not 10%, faster.

Non-reproducible results with MapleCOMSPS

I’ve been reading through the source code of the 2016 SAT Competition Main Track winner, MapleCOMSPS_DRUP, and I found that it has an important piece of code, regulating its behaviour that depends on timing:

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Memory layout of clauses in MiniSat

I have been trying to debug why some MiniSat-based solvers perform better at unit propagation than CryptoMiniSat. It took me exactly 3 full days to find out and I’d like to share this tidbit of information with everyone, as I think it might be of interest and I hardly believe many understand it.

The mystery of the faster unit propagation was that I tried my best to make my solver behave exactly as MiniSat to debug the issue, but even though it was behaving almost identically, it was still slower. It made no sense and I had to dig deeper. You have to understand that both solvers use their own memory managers. In fact, CryptoMiniSat had a memory manager before MiniSat. Memory managers are used so that the clauses are put close to one another so there is a chance that they are in the same memory page, or even better, close enough for them not to waste memory in the cache. This means that a contiguous memory space is reserved where the clauses are placed.

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Testing CryptoMiniSat using GoogleTest

Lately, I have been working quite hard on writing module tests for CryptoMinisat using GoogleTest. I’d like to share what I’ve learnt and what surprised me most about this exercise.

An example

First of all, let me show how a typical test looks like:

Here we are checking that intree probing finds that the set of three binary clauses cause a failure and it enqueues “-2” at top level. If one looks at it, it’s a fairly trivial test. It turns out that most are in fact, fairly trivial if the system is set up well. This test’s setup is the following test fixture:

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